Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input–output data The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels The simulation test results indicate that the proposed algorithms are effective

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-spr.2019.0481

Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises

This paper studies the problem of parameter estimation for the multifrequency sine signals, which have multiple characteristic parameters such as the amplitudes, phases, and frequencies. I...

Hierarchical recursive signal modeling for multifrequency signals based on discrete measured data

Maximum likelihood extended gradient‐based estimation algorithms for the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity

Summary This article considers the parameter estimation for a fractional‐order nonlinear finite impulse response system with colored noise. For the fractional‐order systems, the challenge and diffi...

Iterative parameter and order identification for fractional-order nonlinear finite impulse response systems using the key term separation

Traffic offloading is considered to be a promising technology in the Unmanned Aerial Vehicles- (UAVs-) assisted cellular networks. Due to their selfishness property, UAVs may be reluctant to take part in traffic offloading without any incentive. Moreover, considering the dynamic position of UAVs and the dynamic condition of the transmission channel, it is challenging to design a long-term effective incentive mechanism for multi-UAV networks. In this work, the dynamic contract incentive approach is studied to attract UAVs to participate in traffic offloading effectively. The two-stage contract incentive method is introduced under the information symmetric scenario and the information asymmetric scenario. Considering the sufficient conditions and necessary conditions in the contract design, a sequence optimization algorithm is investigated to acquire the maximum expected utility of the base station. The simulation experiment shows that the designed two-stage dynamic contract improves the performance of traffic offloading effectively.

/pdf/dynamic-contract-incentives-mechanism-for-traffic-offloading-7yh1dxum2o.pdf

Dynamic Contract Incentives Mechanism for Traffic Offloading in Multi-UAV Networks

The invention belongs to the technical field of wireless communication, in particular to a resource distribution and power control combined optimization method based on reinforcement learning in a heterogeneous network. The method aims at dynamic and time-varying characteristics of factors such as transmission channels and transmission power, and an optimal resource distribution, user associationand power control combined strategy is obtained by utilizing a distributed Q learning method through establishing a multi-agent enhanced learning framework in combination with the conditions of user satisfaction degree and operator benefit seeking on the basis of establishing a heterogeneous cellular network system model on the premise that the user selfishness and the operator benefit in the heterogeneous network are considered, so that the maximization of the long-term system utility of the whole network is achieved.

Resource allocation and power control combined optimization method based on reinforcement learning in heterogeneous network

Heterogeneous cellular networks can balance mobile video loads and reduce cell arrangement costs, which is an important technology of future mobile video communication networks. Because of the characteristics of non-convexity of the mobile offloading problem, the design of the optimal strategy is an essential issue. For the sake of ensuring users' quality of service and the long-term overall network utility, this article proposes the distributive optimal method by means of multiple agent reinforcement learning in the downlink heterogeneous cellular networks. In addition, to solve the computational load issue generated by the large action space, deep reinforcement learning is introduced to gain the optimal policy. The learning policy can provide a near-optimal solution efficiently with a fast convergence speed. Simulation results show that the proposed approach is more efficient at improving the performance than the Q-learning method.

Deep Reinforcement Learning for Mobile Video Offloading in Heterogeneous Cellular Networks

Mobile crowdsourcing networks (MCNs) are a promising method of data collecting and processing by leveraging the mobile devices’ sensing and computing capabilities. However, because of the selfish characteristics of the service provider (SP) and mobile users (MUs), crowdsourcing participants only aim to maximize their own benefits. This paper investigates the incentive mechanism between the above two parties to create mutual benefits. By modeling MCNs as a labor market, a contract-based crowdsourcing model with moral hazard is proposed under the asymmetric information scenario. In order to incentivize the potential MUs to participate in crowdsourcing tasks, the optimization problem is formulated to maximize the SP’s utility by jointly examining the crowdsourcing participants’ risk preferences. The impact of crowdsourcing participants’ attitudes of risks on the incentive mechanism has been studied analytically and experimentally. Numerical simulation results demonstrate the effectiveness of the proposed contract design scheme for the crowdsourcing incentive.

https://www.mdpi.com/1999-4893/10/3/104/pdf

Contract-Based Incentive Mechanism for Mobile Crowdsourcing Networks

Heterogeneous networks (HetNets) balance the traffic load and reduce the cost of cell deployment, which is considered as a promising technology in next generation cellular networks. Due to non-convexity characteristics, it is very difficult to obtain the optimal strategy for user association problem. This paper proposes a new framework to ensure the long-term overall network utility under the premise of guaranteeing the quality of service of downlink user equipment in downlink HetNets. At the same time, a distributed optimization algorithm based on multi-user reinforcement learning is proposed. In order to solve the problem of large computational load of big action space, the optimal strategy is obtained by introducing the method of deep Q-network (DQN). Simulation results show that DQN has better performance than Q-learning method.

Nan Zhao

Papers

Dynamic Contract Incentives Mechanism for Traffic Offloading in Multi-UAV Networks

Resource allocation and power control combined optimization method based on reinforcement learning in heterogeneous network

Deep Reinforcement Learning for Mobile Video Offloading in Heterogeneous Cellular Networks

Contract-Based Incentive Mechanism for Mobile Crowdsourcing Networks

Deep Q-Network for User Association in Heterogeneous Cellular Networks